Developing spatially explicit methods to enhance geovisual analytics and decision support

当面临重要决策时，我们试图将决策建立在一个理性的框架上，这个框架通常包括多个决策标准。多准则决策分析（MCDA）在近20年前首次引入地理信息系统（GIS）领域。基于地理信息系统的军事和民防资源评估目前在各种应用领域进行，包括环境、交通和城市/区域规划、废物管理以及水文、农业和林业。 然而，在基于GIS的MCDA中，除了评价结果在地图上的可视化外，很少使用位置特征。例如，既没有一个普遍接受的框架，空间明确的决策标准，也没有一个系统的方法，将空间变化的标准权重或决策规则。军事和民防资源的计算通常基于非空间标准，不包括空间上明确的参数或决策者的偏好。 拟议中的研究旨在缩小这一概念差距，并展示在选定的应用程序中的多标准决策的空间显式方法的效用。我的团队将把地理可视化和军事和民防资源工具与空间统计结合起来，并制定一个以地图为中心的综合方法，指导用户完成社区服务规划和环境评估等领域的有效决策任务。我们还将研究决策标准，标准标准化和标准加权的空间变化对基于GIS的MCDA结果的影响，并开发一个时空MCDA框架。 这项研究有助于在地理信息学的知识体系。它有可能使基于地理信息系统的军事和民防资源评估更加有效，并解决空间决策支持中可能阻碍其更广泛接受的概念问题。最终，该研究计划将改善政府，非营利组织和企业的战略决策，其中决策具有地理空间组成部分。

When faced with important decisions, we try to base our decision-making on a rational framework which often includes multiple decision criteria. Multi-criteria decision analysis (MCDA) was first introduced to the field of Geographic Information Systems (GIS) nearly 20 years ago. GIS-based MCDA is now conducted in various fields of application, including environmental, transportation, and urban/regional planning, in waste management, as well as in hydrology, agriculture, and forestry. Paradoxically, there has been little use of the locational characteristics in GIS-based MCDA besides the visualization of evaluation results on maps. For example, there is neither a generally accepted framework for spatially explicit decision criteria nor a systematic approach to incorporating spatial variations in criterion weights or decision rules. MCDA calculations are typically based on non-spatial criteria and do not include spatially explicit parameters or decision-maker preferences. The proposed research aims at closing this conceptual gap and demonstrating the utility of spatially explicit methods for multi-criteria decision-making in selected applications. My team will integrate geovisualization and MCDA tools with spatial statistics and develop a comprehensive map-centred methodology that guides users through valid sequences of decision-making tasks in fields such as community service planning and environmental assessments. We will also study the effects of spatial variations in decision criteria, criterion standardization, and criterion weighting on GIS-based MCDA results, and develop a framework for spatio-temporal MCDA. This research contributes to the body of knowledge in Geomatics. It has the potential to make GIS-based MCDA more effective and addresses conceptual issues in spatial decision support that may have hampered its broader acceptance. Ultimately, this research program will improve strategic decision-making by government, non-profit organizations, and businesses, where decisions have a geospatial component.